Methods And Systems For Overlay Measurement Based On Soft X-Ray Scatterometry

ABSTRACT

Methods and systems for performing overlay and edge placement errors based on Soft X-Ray (SXR) scatterometry measurement data are presented herein. Short wavelength SXR radiation focused over a small illumination spot size enables measurement of design rule targets or in-die active device structures. In some embodiments, SXR scatterometry measurements are performed with SXR radiation having energy in a range from 10 to 5,000 electronvolts. As a result, measurements at SXR wavelengths permit target design at process design rules that closely represents actual device overlay. In some embodiments, SXR scatterometry measurements of overlay and shape parameters are performed simultaneously from the same metrology target to enable accurate measurement of Edge Placement Errors. In another aspect, overlay of aperiodic device structures is estimated based on SXR measurements of design rule targets by calibrating the SXR measurements to reference measurements of the actual device target.

CROSS REFERENCE TO RELATED APPLICATION

The present application for patent claims priority under 35 U.S.C. § 119from U.S. provisional patent application Ser. No. 62/958,089, entitled“System and Method for Measuring Overlay and Edge Placement Error WithSoft X-ray Scatterometry,” filed Jan. 7, 2020, the subject matter ofwhich is incorporated herein by reference in its entirety.

TECHNICAL FIELD

The described embodiments relate to metrology systems and methods, andmore particularly to methods and systems for improved measurementaccuracy.

BACKGROUND INFORMATION

Semiconductor devices such as logic and memory devices are typicallyfabricated by a sequence of processing steps applied to a specimen. Thevarious features and multiple structural levels of the semiconductordevices are formed by these processing steps. For example, lithographyamong others is one semiconductor fabrication process that involvesgenerating a pattern on a semiconductor wafer. Additional examples ofsemiconductor fabrication processes include, but are not limited to,chemical-mechanical polishing, etch, deposition, and ion implantation.Multiple semiconductor devices may be fabricated on a singlesemiconductor wafer and then separated into individual semiconductordevices.

Metrology processes are used at various steps during a semiconductormanufacturing process to detect defects on wafers to promote higheryield. A number of metrology based techniques including scatterometryand reflectometry implementations and associated analysis algorithms arecommonly used to characterize critical dimensions, film thicknesses,composition and other parameters of nanoscale structures.

As devices (e.g., logic and memory devices) move toward smallernanometer-scale dimensions, characterization becomes more difficult.Devices incorporating complex three-dimensional geometry and materialswith diverse physical properties contribute to characterizationdifficulty. For example, modern memory structures are often high-aspectratio, three-dimensional structures that make it difficult for opticalradiation to penetrate to the bottom layers. Optical metrology toolsutilizing infrared to visible light can penetrate many layers oftranslucent materials, but longer wavelengths that provide good depth ofpenetration do not provide sufficient sensitivity to small anomalies.Similarly, electron based metrology tools suffer from inadequate depthof penetration without damaging the sample. In addition, the increasingnumber of parameters required to characterize complex structures (e.g.,FinFETs), leads to increasing parameter correlation. As a result, theparameters characterizing the target often cannot be reliably decoupledwith available measurements. For some structural parameters, such asedge placement error (EPE), there is currently no high throughput (e.g.,optical) measurement solution.

Currently, several technologies are employed to measure overlay andcritical dimensions (CD) with varying levels of success. Optical andelectron beam metrology techniques are employed to perform CD andoverlay measurements, typically on specialized metrology targets.

Optical measurements of overlay are predominantly based on eitheroptical imaging or non-imaging diffraction (scatterometry). However,these approaches have not reliably overcome fundamental challengesassociated with measurement of many advanced targets (e.g., complex 3Dstructures, structures smaller than 10 nm, structures employing opaquematerials) and measurement applications (e.g., line edge roughness andline width roughness measurements).

Using existing methods, overlay error is typically evaluated based onmeasurements of specialized target structures formed at variouslocations on the wafer by a lithography tool. In some examples,spatially separated gratings are employed for imaging-based opticaloverlay measurements. In some other examples, box in box structures areemployed for imaging-based optical overlay measurements. In this form, abox is created on one layer of the wafer and a second, smaller box iscreated on another layer. The localized overlay error is measured bycomparing the alignment between the centers of the two boxes. Suchmeasurements are taken at locations on the wafer where target structuresare available. In some examples, overlapping gratings or interleavedgratings are employed for scatterometry-based optical overlaymeasurements or electron-beam overlay measurements.

Unfortunately, these specialized target structures often do not conformto the design rules of the particular semiconductor manufacturingprocess being employed to generate the electronic device. This leads toerrors in estimation of overlay errors associated with actual devicestructures that are manufactured in accordance with the applicabledesign rules.

In one example, image-based optical overlay metrology is severelylimited by the resolution of imaging at optical wavelengths. Thus, onlytargets with features much larger than the design rule can be measured.Image-based optical overlay metrology often requires the pattern to beresolved with an optical microscope that requires thick lines withcritical dimensions far exceeding design rule critical dimensions.

In another example, scatterometry-based optical overlay metrology basedon 0th order diffraction has very low sensitivity to small overlayerrors as the sensitivity decreases with the pitch of the periodictargets. This drives the pitch to much larger dimensions than the designrule of the device. Moreover, the accuracy of this measurement approachdegrades dramatically in the presence of any asymmetry in any of thelayers where overlay is measured. In addition, this approach cannotdifferentiate between positive and negative overlay errors in a singlemeasurement.

In another example, scatterometry-based optical overlay metrology basedon diffraction orders higher than zero also require relatively largepitch targets to generate sufficient signal at nonzero propagatingdiffraction orders. An overlay asymmetry optical signal is typicallygenerated from periodic targets having a pattern pitch comparable to theoptical wavelength of the illumination light. In some examples, pitchvalues in the range 500-800 nm may be used. Meanwhile, actual devicepitches for logic or memory applications (design rule dimensions) aremuch smaller, e.g., in the range 100-400 nm, or even below 100 nm. Inaddition, the accuracy of this approach degrades dramatically in thepresence of any asymmetry in any of the layers where overlay ismeasured. Decreasing the optical wavelength of the illumination into thedeep ultraviolet and vacuum ultraviolet range does not help as thesephotons are attenuated and do not sufficiently penetrate multiple layerstructures to reach underlying patterns required to evaluate overlay andedge placement error.

Electron based metrology techniques such as Scanning Electron Microscopy(SEM) and E-beam metrology are able to resolve nanometer-scale featuresand measure non-periodic structures such as random logic. However,electron based metrology systems are destructive when employed tomeasure actual devices. In addition, electron based metrology systemsare low throughput. Measurement times may be on the order of a fewseconds per measurement site. In addition, electron based metrologysystems are top-down imaging systems that provide very limited threedimensional measurement capability. For example, when SEM is employed tomeasure overlay between overlapping gratings, it loses the ability tomeasure CD and EPE due to increased point spread function withpenetration depth. Further details are described in U.S. Pat. No.10,473,460 to Gutman et al., and assigned to KLA-Tencor Corporation, thecontent of which is incorporated herein by reference in its entirety. Ingeneral, SEM achieves intermediate resolution levels, but is unable topenetrate structures to sufficient depth without destroying the sample.In addition, the required charging of the specimen has an adverse effecton imaging performance.

Atomic force microscopes (AFM) and scanning-tunneling microscopes (STM)are able to achieve atomic resolution, but they can only probe thesurface of the specimen. In addition, AFM and STM microscopes requirelong scanning times.

Transmission electron microscopes (TEM) achieve high resolution levelsand are able to probe arbitrary depths, but TEM requires destructivesectioning of the specimen.

To overcome some limitations of optical and electron based metrology formeasurement of overlay and EPE, an absolute registration measurementtechnique may be employed. An accurate translation stage having a rangeequal to the spatial separation between any two patterns is employed tomeasure the absolute distance between features. This result is employedto assist in the evaluation of overlay, CD, and EPE. Unfortunately, theabsolute registration measurement technique requires an accurate stage,adds complexity to the measurement tool, and limits throughput.Furthermore, the technique may not perform well on pattern-over-patterntargets or device structures. Further details are described in WIPOPublication No. 2019/173171 by Shchegrov et al., and assigned toKLA-Tencor Corporation, the content of which is incorporated herein byreference in its entirety.

In summary, semiconductor device yield at device fabrication nodes below20 nanometers for logic devices and advanced DRAM, and vertical orplanar NAND devices is a complex function of many parameters, includingfilm thicknesses, profile parameters of patterned lines, overlay errors,and edge placement errors (EPE). Of these, EPE has the most demandingprocess window and requires metrology and control of CD and overlay.Currently there is no high-throughput, optical metrology solution forEPE measurements and many design-rule overlay measurement applications.In addition, the absence of adequate metrology makes it challenging todefine control schemes to improve device yield.

Future metrology applications present challenges for metrology due toincreasingly small resolution requirements, multi-parameter correlation,increasingly complex geometric structures, and increasing use of opaquematerials. Thus, methods and systems for improved overlay and shapemeasurements are desired.

SUMMARY

Methods and systems for performing overlay and edge placement errorsbased on Soft X-Ray (SXR) scatterometry measurement data are presentedherein. Short wavelength SXR radiation focused over a small illuminationspot size enables measurement of design rule targets, i.e., targetshaving same or approximately the same pitch as nearby in-die activedevice structures, or in-die active device structures themselves. Inaddition to providing overlay metrology capability, the methods andsystems described herein enhance the precision and accuracy of shapeparameter measurements by strongly de-correlating geometric parametersof the measured structures.

SXR illumination radiation enables penetration into opaque areas andunder-layers of a target. In some embodiments, SXR scatterometrymeasurements are performed with SXR radiation having energy in a rangefrom 10 to 5,000 electronvolts. Typically, diffraction limits and otheroptical effects control the minimum possible target size for shape andoverlay measurements. Due to the relatively short wavelengths of SXRillumination, SXR scatterometry measurements can be performed onmetrology targets having a relatively small target area.

SXR penetration to underlayers enables SXR scatterometry measurementswith relatively high sensitivity to signals required to estimateoverlay, CD, and EPE. Furthermore, SXR scatterometry overlay measurementof design rule targets more closely represents actual device overlay,compared to traditional overlay targets having much larger pitch. SXRscatterometry enables overlay measurements on design-rule targetsbecause the illumination wavelength(s) are shorter than the period ofthe measured design-rule targets. As a result, measurements at SXRwavelengths permits target design at process design rules.

In some examples, SXR scatterometry measurements of overlay are based ondirect measurement of actual device structures, e.g., SRAM.

In some embodiments, the design rule targets include multiple layerseach having an underlying periodicity. SXR scatterometry enablesmeasurement of multiple layer design rule targets with high sensitivityto underlayer patterns. In some of these embodiments, the top layer ofthe design rule target is a photoresist layer. In this manner, SXRscatterometry enables After-Develop Inspection (ADI) process monitoring.

In one aspect, a SXR scatterometry system is configured to estimateoverlay error between different layers of a design rule metrology targetor in-die active device structures from non-zero diffraction ordersscattered from the structure under measurement. Due to the relativelyshort wavelengths of SXR radiation, nonzero diffraction orders, and the+/−1 diffraction orders in particular, provide relatively highsensitivity to overlay error.

In some embodiments, SXR scatterometry measurements of overlay and shapeparameters are performed simultaneously from SXR scatterometrymeasurements collected from the same metrology target. This enablesmeasurements of Edge Placement Errors (EPE), such as end lineshortening, line to contact distance, etc. As a result, SXRscatterometry enables edge placement error (EPE) measurements withouterrors due to target bias, which occur when overlay and CD measurementsare performed on different targets. In addition, simultaneousmeasurement of overlay and CD structure parameters from the samemetrology target improves both measurement accuracy and throughput.

In some embodiments, SXR scatterometry measurement signals from twocells of a metrology target each having a nominal offset in oppositedirections are employed to resolve overlay error.

In some examples, metrology based on SXR scatterometry involvesdetermining parameters of interest, e.g., overlay error, shapeparameters, etc., characterizing the sample by the inverse solution of apre-determined measurement model with the measured SXR scatterometrydata. In this manner, target parameters are estimated by solving forvalues of a parameterized measurement model that minimize errors betweenthe measured scattered x-ray intensities and modeled results.

In some embodiments, the value of overlay error associated with ameasurement target or an in-die target is directly determined fromdetected intensities within one or more nonzero diffraction orders basedon a trained machine-learning based measurement model. In theseembodiments, a trained machine learning based model directly extractsoverlay error from SXR measurement data.

In some embodiments, a SXR scatterometry based overlay measurementinvolves illuminating a sample with SXR radiation and detecting theintensities of the resulting diffraction orders for multiple angles ofincidence relative to the sample, multiple wavelengths, or both.Furthermore, the overlay error associated with the measurement target isdetermined based on modulations in the plurality of intensities withineach of the one or more nonzero diffraction orders at each of themultiple measurement instances.

In another aspect, the actual device target is aperiodic. By calibratingoverlay measurements to a reference measurement, SXR scatterometrytechniques can be applied to estimate overlay of aperiodic structuresbased on measurements of design rule targets with sufficientperiodicity. This effectively overcomes the limitation of scatterometricmeasurements requiring the measured target to be periodic orapproximately periodic.

In some embodiments, a multiple layer overlay metrology target isdesigned with different pitch at different layers such that adiffraction order arising from one layer constructively interferes witha different diffraction order of another layer. Conversely, intensitymeasurements detected at different order number pairs not subject toconstructive interference in overlay are dominated by shape parameters.Thus, in some embodiments, a metrology overlay target is designed withspecific grating structures to increase sensitivity to overlay atspecific grating order pairs, and also provide intensity data useful forestimation of shape parameter values.

In some embodiments, a multiple layer overlay metrology target isdesigned with different pitch orientations at different layers such thata diffraction order arising from one layer constructively interfereswith a different diffraction order of another layer. In general, a setof layers having different periodicities (e.g., different gratingpitches), different pitch orientations, or any combination thereof,gives rise to a set of scattering vectors, each associated with adifferent layer. The overlay metrology target is designed such that apredetermined subset of the scattering vectors are aligned. In thismanner, the sensitivity to overlay among the layers corresponding withthe predetermined subset of scattering vectors is enhanced.

The foregoing is a summary and thus contains, by necessity,simplifications, generalizations and omissions of detail; consequently,those skilled in the art will appreciate that the summary isillustrative only and is not limiting in any way. Other aspects,inventive features, and advantages of the devices and/or processesdescribed herein will become apparent in the non-limiting detaileddescription set forth herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrative of a hardmask pattern of linestructures 11 fabricated in a static random access memory (SRAM) area 10of a microelectronic chip.

FIG. 2 is a diagram illustrative of a bottom anti-reflective coating(BARC) layer 12 and a resist layer 13 disposed on top of the pattern ofline structures depicted in FIG. 1.

FIG. 3 is a diagram illustrative of an embodiment of an Soft X-Ray (SXR)metrology tool 100 for measuring characteristics of a specimen inaccordance with the exemplary methods presented herein.

FIG. 4 is a diagram illustrative of a top view of device structure 400that includes active fields 401-404, gates 405-408, and contacts409-421.

FIG. 5A depicts a cell having stacked grating structures offset from oneanother by a distance equal to the sum of a nominal offset in onedirection, +OFFSET, and an overlay error, OVL.

FIG. 5B depicts a cell having stacked grating structures offset from oneanother by a distance equal to the sum of a nominal offset in adirection opposite the nominal offset depicted in FIG. 5A, −OFFSET, andthe overlay error, OVL.

FIG. 6 is a diagram illustrative of a model building and analysis engine180 configured to estimate overlay based on a model fitting analysis asdescribed herein.

FIG. 7 is a diagram illustrative of a wafer 101 including a metrologytarget 120 illuminated by a beam of x-ray radiation at an angle ofincidence and azimuth angle.

FIG. 8 depicts a layered metrology target 150 including two periodicarrays of lines 151 and 152 in different layers.

FIG. 9 depicts a plot 170 indicative of a simulation of a fitting of asimplified model of intensity and corresponding measured values of the−2 and +2 diffraction orders for a range of angles for the metrologytarget illustrated in FIG. 8.

FIG. 10 is a diagram illustrative of a semiconductor die includingactive device structures fabricated in an active device area and designrule metrology targets fabricated in a scribe line area.

FIG. 11 is a flowchart illustrative of an exemplary method 200 ofestimating overlay based on calibrated SXR measurements as describedherein.

DETAILED DESCRIPTION

Reference will now be made in detail to background examples and someembodiments of the invention, examples of which are illustrated in theaccompanying drawings.

FIG. 1 depicts a hardmask pattern of line structures 11 fabricated in astatic random access memory (SRAM) area 10 of a microelectronic chip.The complex layout of the active region is created by combining multiplepatterning techniques with cut masks. Cut masks selectively removeportions of the hardmask layer that is used to pattern the substrateinto active regions. FIG. 2 depicts a bottom anti-reflective coating(BARC) layer 12 and a resist layer 13 disposed on top of the pattern ofline structures depicted in FIG. 1. The resist layer is used toselectively remove part of the hardmask pattern below the openings 14 ofthe resist layer 13. As depicted in FIG. 1, the hardmask pattern of linestructures 11 is buried by the BARC layer 12, even within the openings14 of the resist layer 13.

To provide adequate yield for the cut mask process, reliablemeasurements for shape parameters (e.g., CD, HT, SWA, profile parameter,etc.), film thicknesses, and overlay are required. A calculation ofoverlay reveals that it is a function of many structural parameters fromprevious steps of a quadruple patterning process. The distribution ofthe gap between the edge of the cut and the adjacent line structure, andhence the yield of the process, depends on a complex interaction of allthe process parameters.

In another example, edge placement distance (EPD) and the associatededge placement error (EPE) is an important parameter to monitor andcontrol after device electrical contacts are made. The differencebetween the desired and the actual EPD is called EPE. EPD and EPE are afunction of both overlay and CD errors.

Methods and systems for performing overlay and edge placement errors ofstructures and materials based on Soft X-Ray (SXR) scatterometrymeasurement data are presented. Short wavelength SXR radiation focusedover a small illumination spot size enables measurement of design ruletargets, i.e., targets having same or approximately the same pitch asnearby in-die active device structures, or in-die active devicestructures themselves. The methods and systems presented herein may beapplied to two and three dimensional design rule metrology targets,located within or outside of functional die. In addition to providingoverlay metrology capability, the methods and systems described hereinenhance the precision and accuracy of shape parameter measurements bystrongly de-correlating geometric parameters of the measured structures.

SXR illumination radiation enables penetration into opaque areas andunder-layers of a target. Examples of measureable geometric parametersusing SXR scatterometry includes pore size, pore density, line edgeroughness, line width roughness, side wall angle, profile, criticaldimension, overlay, edge placement error, and pitch. Examples ofmeasureable material parameters include electron density, elementalidentification and composition. In some examples, SXR scatterometryenables the measurement of features smaller than 10 nm as well asadvanced semiconductor structures such as spin-transfer-torque MRAMwhere measurements of geometrical parameters and material parameters areneeded.

In some embodiments, SXR scatterometry measurements are performed withSXR radiation having energy in a range from 10 to 5,000 electronvolts.Typically, diffraction limits and other optical effects control theminimum possible target size for shape and overlay measurements. Due tothe relatively short wavelengths of SXR illumination, SXR scatterometrymeasurements can be performed on metrology targets having a relativelysmall target area. In some embodiments, SXR scatterometry measurementsare performed on metrology targets over an area having a dimension ofmaximum extent less than 5 micrometers. In some embodiments, SXRscatterometry measurements are performed on metrology targets over anarea having a dimension of maximum extent less than 2 micrometers.

In some embodiments, the design rule targets include multiple layerseach having an underlying periodicity. SXR scatterometry enablesmeasurement of multiple layer design rule targets with high sensitivityto underlayer patterns. In some of these embodiments, the top layer ofthe design rule target is a photoresist layer. In this manner, SXRscatterometry enables After-Develop Inspection (ADI) process monitoring.

SXR penetration to underlayers enables SXR scatterometry measurementswith relatively high sensitivity to signals required to estimateoverlay, CD, and EPE. Furthermore, SXR scatterometry overlay measurementof design rule targets more closely represents actual device overlay,compared to traditional overlay targets having much larger pitch. Insome examples, SXR scatterometry measurements of overlay are based ondirect measurement of actual device structures, e.g., SRAM.

In one aspect, a SXR scatterometry system is configured to estimateoverlay error between different layers of a design rule metrology targetor in-die active device structures from non-zero diffraction ordersscattered from the structure under measurement.

FIG. 3 illustrates an embodiment of a SXR scatterometry tool 100 formeasuring characteristics of a specimen in at least one novel aspect. Asshown in FIG. 3, the system 100 may be used to perform SXR scatterometrymeasurements over a measurement area 102 of a specimen 101 illuminatedby an incident illumination beam spot.

In the depicted embodiment, metrology tool 100 includes an x-rayillumination source 110, focusing optics 111, beam divergence controlslit 112, and slit 113. The x-ray illumination source 110 is configuredto generate SXR radiation suitable for SXR scatterometry measurements.In some embodiments, X-ray illumination source 110 is a polychromatic,high-brightness, large etendue source. In some embodiments, the x-rayillumination source 110 is configured to generate x-ray radiation in arange between 10-5000 electron-volts. In general, any suitablehigh-brightness x-ray illumination source capable of generating highbrightness SXR at flux levels sufficient to enable high-throughput,inline metrology may be contemplated to supply x-ray illumination forSXR measurements.

In some embodiments, an x-ray source includes a tunable monochromatorthat enables the x-ray source to deliver x-ray radiation at different,selectable wavelengths. In some embodiments, one or more x-ray sourcesare employed to ensure that the x-ray source supplies light atwavelengths that allow sufficient penetration into the specimen undermeasurement.

In some embodiments, illumination source 110 is a high harmonicgeneration (HHG) x-ray source. In some other embodiments, illuminationsource 110 is a wiggler/undulator synchrotron radiation source (SRS). Anexemplary wiggler/undulator SRS is described in U.S. Pat. Nos. 8,941,336and 8,749,179, the contents of which are incorporated herein byreference in their entireties.

In some other embodiments, illumination source 110 is a laser producedplasma (LPP) light source. In some of these embodiments the LPP lightsource includes any of Xenon, Krypton, Argon, Neon, and Nitrogenemitting materials. In general, the selection of a suitable LPP targetmaterial is optimized for brightness in resonant S×R regions. Forexample, plasma emitted by Krypton provides high brightness at theSilicon K-edge. In another example, plasma emitted by Xenon provideshigh brightness throughout the entire S×R region of (10-5000 eV).

LPP target material selection may also be optimized for reliable andlong lifetime light source operation. Noble gas target materials such asXenon, Krypton, and Argon are inert and can be reused in a closed loopoperation with minimum or no decontamination processing. An exemplarySXR illumination source is described in U.S. Patent Publication No.2019/0215940 by Khodykin et al., and assigned to KLA-Tencor Corporation,the content of which is incorporated herein by reference in itsentirety.

In some embodiments, the wavelengths emitted by the illumination source(e.g., illumination source 110) are selectable. In some embodiments,illumination source 110 is a LPP light source controlled by computingsystem 130 to maximize flux in one or more selected spectral regions.Laser peak intensity at the target material controls the plasmatemperature and thus the spectral region of emitted radiation. Laserpeak intensity is varied by adjusting pulse energy, pulse width, orboth. In one example, a 100 picosecond pulse width is suitable forgenerating SXR radiation. As depicted in FIG. 3, computing system 130communicates command signals 136 to illumination source 110 that causeillumination source 110 to adjust the spectral range of wavelengthsemitted from illumination source 110. In one example, illuminationsource 110 is a LPP light source, and the LPP light source adjusts anyof a pulse duration, pulse frequency, and target material composition torealize a desired spectral range of wavelengths emitted from the LPPlight source.

By way of non-limiting example, any of a particle accelerator source, aliquid anode source, a rotating anode source, a stationary, solid anodesource, a microfocus source, a microfocus rotating anode source, aplasma based source, and an inverse Compton source may be employed asx-ray illumination source 110.

Exemplary x-ray sources include electron beam sources configured tobombard solid or liquid targets to stimulate x-ray radiation. Methodsand systems for generating high brightness, liquid metal x-rayillumination are described in U.S. Pat. No. 7,929,667, issued on Apr.19, 2011, to KLA-Tencor Corp., the entirety of which is incorporatedherein by reference.

X-ray illumination source 110 produces x-ray emission over a source areahaving finite lateral dimensions (i.e., non-zero dimensions orthogonalto the beam axis. In one embodiment, the source area of illuminationsource 110 is characterized by a lateral dimension of less than 20micrometers. In some embodiments, the source area is characterized by alateral dimension of 10 micrometers or less. Small source size enablesillumination of a small target area on the specimen with highbrightness, thus improving measurement precision, accuracy, andthroughput.

In general, x-ray optics shape and direct x-ray radiation to specimen101. In some examples, the x-ray optics collimate or focus the x-raybeam onto measurement area 102 of specimen 101 to less than 1milliradian divergence using multilayer x-ray optics. In someembodiments, the x-ray optics include one or more x-ray collimatingmirrors, x-ray apertures, x-ray beam stops, refractive x-ray optics,diffractive optics such as zone plates, Schwarzschild optics,Kirkpatrick-Baez optics, Montel optics, Wolter optics, specular x-rayoptics such as ellipsoidal mirrors, polycapillary optics such as hollowcapillary x-ray waveguides, multilayer optics or systems, or anycombination thereof. Further details are described in U.S. PatentPublication No. 2015/0110249, the content of which is incorporatedherein by reference it its entirety.

As depicted in FIG. 3, focusing optics 111 focuses source radiation ontoa metrology target located on specimen 101. The finite lateral sourcedimension results in finite spot size 102 on the target defined by therays 116 coming from the edges of the source and any beam shapingprovided by beam slits 112 and 113.

In some embodiments, focusing optics 111 include elliptically shapedfocusing optical elements. In the embodiment depicted in FIG. 3, themagnification of focusing optics 111 at the center of the ellipse isapproximately one. As a result, the illumination spot size projectedonto the surface of specimen 101 is approximately the same size as theillumination source, adjusted for beam spread due to the nominalincidence angle, G.

In a further aspect, focusing optics 111 collect source emission andselect one or more discrete wavelengths or spectral bands, and focus theselected light onto specimen 101 at a desired nominal angle ofincidence.

The nominal incidence angle is selected to achieve a desired penetrationof the metrology target to maximize signal information content whileremaining within metrology target boundaries. The critical angle of hardx-rays is very small, but the critical angle of soft x-rays issignificantly larger. As a result of this additional measurementflexibility SXR measurements probe more deeply into the structure withless sensitivity to the precise value of the nominal incidence angle.

In some embodiments, focusing optics 111 include graded multi-layersthat select desired wavelengths or ranges of wavelengths for projectiononto specimen 101. In some examples, focusing optics 111 includes agraded multi-layer structure (e.g., layers or coatings) that selects onewavelength and projects the selected wavelength onto specimen 101 over arange of angles of incidence about the nominal angle of incidence. Insome examples, focusing optics 111 includes a graded multi-layerstructure that selects a range of wavelengths and projects the selectedwavelengths onto specimen 101 over one angle of incidence. In someexamples, focusing optics 111 includes a graded multi-layer structurethat selects a range of wavelengths and projects the selectedwavelengths onto specimen 101 over a range of angles of incidence.

Graded multi-layered optics are preferred to minimize loss of light thatoccurs when single layer grating structures are too deep. In general,multi-layer optics select reflected wavelengths. The spectral bandwidthof the selected wavelengths optimizes flux provided to specimen 101,information content in the measured diffracted orders, and preventsdegradation of signal through angular dispersion and diffraction peakoverlap at the detector. In addition, graded multi-layer optics areemployed to control divergence. Angular divergence at each wavelength isoptimized for flux and minimal spatial overlap at the detector.

In some examples, graded multi-layer optics select wavelengths toenhance contrast and information content of diffraction signals fromspecific material interfaces or structural dimensions. For example, theselected wavelengths may be chosen to span element-specific resonanceregions (e.g., Silicon K-edge, Nitrogen, Oxygen K-edge, etc.). Inaddition, in these examples, the illumination source may also be tunedto maximize flux in the selected spectral region (e.g., HHG spectraltuning, LPP laser tuning, etc.)

In some embodiments, focusing optics 111 include a plurality ofreflective optical elements each having an elliptical surface shape.Each reflective optical element includes a substrate and a multi-layercoating tuned to reflect a different wavelength or range of wavelengths.In some embodiments, a plurality of reflective optical elements (e.g.,1-5) each reflecting a different wavelength or range of wavelengths arearranged at each angle of incidence. In a further embodiment, multiplesets (e.g., 2-5) of reflective optical elements each reflecting adifferent wavelength or range of wavelengths are arranged at a differentnominal angle of incidence. In some embodiments, the multiple sets ofreflective optical elements simultaneously project illumination lightonto specimen 101 during measurement. In some other embodiments, themultiple sets of reflective optical elements sequentially projectillumination light onto specimen 101 during measurement. In theseembodiments, active shutters or apertures are employed to control theillumination light projected onto specimen 101.

In some embodiments, the ranges of wavelengths, AOI, Azimuth, or anycombination thereof, projected onto the same metrology area, areadjusted by actively positioning one or more mirror elements of thefocusing optics. As depicted in FIG. 3, computing system 130communicates command signals 137 to actuator system 115 that causesactuator system 115 to adjust the position, alignment, or both, of oneor more of the optical elements of focusing optics 111 to achieve thedesired ranges of wavelengths, AOI, Azimuth, or any combination thereof,projected onto specimen 101.

In general, the angle of incidence is selected for each wavelength tooptimize penetration and absorption of the illumination light by themetrology target under measurement. In many examples, multiple layerstructures are measured and angle of incidence is selected to maximizesignal information associated with the desired layers of interest. Inthe example of overlay metrology, the wavelength(s) and angle(s) ofincidence are selected to maximize signal information resulting frominterference between scattering from the previous layer and the currentlayer. In addition, azimuth angle is also selected to optimize signalinformation content. In addition, azimuth angle is selected to ensureangular separation of diffraction peaks at the detector.

In some embodiments, a SXR scatterometry system (e.g., metrology tool100) includes one or more beam slits or apertures to shape theillumination beam 114 incident on specimen 101 and selectively block aportion of illumination light that would otherwise illuminate ametrology target under measurement. One or more beam slits define thebeam size and shape such that the x-ray illumination spot fits withinthe area of the metrology target under measurement. In addition, one ormore beam slits define illumination beam divergence to minimize overlapof diffraction orders on the detector.

In some embodiments, a SXR scatterometry system (e.g., metrology tool100) includes one or more beam slits or apertures to select a set ofillumination wavelengths that simultaneously illuminate a metrologytarget under measurement. In these embodiments, one or more slits areconfigured to pass illumination including multiple illuminationwavelengths. In general, simultaneous illumination of a metrology targetunder measurement is preferred to increase signal information andthroughput. However, in practice, overlap of diffraction orders at thedetector limits the range of illumination wavelengths. In someembodiments, one or more slits are configured to sequentially passdifferent illumination wavelengths. In some examples, sequentialillumination at larger angular divergence provides higher throughputbecause the signal to noise ratio for sequential illumination may behigher compared to simultaneous illumination when beam divergence islarger. When measurements are performed sequentially the problem ofoverlap of diffraction orders is not an issue. This increasesmeasurement flexibility and improves signal to noise ratio.

FIG. 3 depicts a beam divergence control slit 112 located in the beampath between focusing optics 111 and beam shaping slit 113. Beamdivergence control slit 112 limits the divergence of the illuminationprovided to the specimen under measurement. Beam shaping slit 113 islocated in the beam path between beam divergence control slit 112 andspecimen 101. Beam shaping slit 113 further shapes the incident beam 114and selects the illumination wavelength(s) of incident beam 114. Beamshaping slit 113 is located in the beam path immediately before specimen101. In some embodiments, the slits of beam shaping slit 113 are locatedin close proximity to specimen 101 to minimize the enlargement of theincident beam spot size due to beam divergence defined by finite sourcesize. As depicted in FIG. 3, computing system 130 communicates commandsignals 138 to beam divergence control slit 112 that causes activeelements of the beam divergence control slit 112 to adjust the position,alignment, or both, of one or more of the optical elements of beamdivergence control slit 112 to achieve the desired beam divergence.

Similarly, as depicted in FIG. 3, computing system 130 communicatescommand signals 139 to beam shaping slit 113 that causes active elementsof the beam shaping slit 113 to adjust the position, alignment, or both,of one or more of the optical elements of beam shaping slit 113 toachieve the desired beam shape projected onto specimen 101.

In some embodiments, beam shaping slit 113 includes multiple,independently actuated beam shaping slits. In one embodiment, beamshaping slit 113 includes four independently actuated beam shapingslits. These four beams shaping slits effectively block a portion of theincoming beam and generate an illumination beam 114 having a box shapedillumination cross-section.

Slits of beam shaping slit 113 are constructed from materials thatminimize scattering and effectively block incident radiation. Exemplarymaterials include single crystal materials such as Germanium, GalliumArsenide, Indium Phosphide, etc. Typically, the slit material is cleavedalong a crystallographic direction, rather than sawn, to minimizescattering across structural boundaries. In addition, the slit isoriented with respect to the incoming beam such that the interactionbetween the incoming radiation and the internal structure of the slitmaterial produces a minimum amount of scattering. The crystals areattached to each slit holder made of high density material (e.g.,tungsten) for complete blocking of the x-ray beam on one side of theslit.

In some embodiments, the focusing optics of an SXR scatterometry systemprojects an image of the illumination source onto the specimen undermeasurement with a demagnification of at least five (i.e., magnificationfactor of 0.2 or less). In some embodiments, an SXR scatterometry systemas described herein employs a SXR illumination source having a sourcearea characterized by a lateral dimension of 20 micrometers or less(i.e., source size is 20 micrometers or smaller). In some embodiments,focusing optics are employed with a demagnification factor of at leastfive (i.e., project an image of the source onto the wafer that is fivetimes smaller than the source size) to project illumination onto aspecimen with an incident illumination spot size of four micrometers orless.

In some embodiments, illumination source 110 is an LPP light sourcehaving a source size of 10 micrometers, or less, and focusing optics 111have a demagnification factor of approximately 10. This enables an SXRscatterometry tool to focus illumination light onto a metrology targethaving dimensions of 1-2 micrometers. The ability to measure targetshaving dimensions of 1-2 micrometers reduces the wafer area committed tospecialized metrology targets. In addition, the ability to measuretargets having dimensions of 1-2 micrometers enables the directmeasurement of device structures, rather than specialized metrologytargets. Measuring device structures directly eliminatestarget-to-device bias. This significantly improves measurement quality.In addition, measurements of in-die targets enable characterization ofparameter variation within-die. Exemplary parameters of interest includecritical dimensions, overlay, and edge placement errors.

X-ray detector 119 collects x-ray radiation 118 scattered from specimen101 and generates output signals 135 indicative of properties ofspecimen 101 that are sensitive to the incident x-ray radiation inaccordance with a SXR scatterometry measurement modality. In someembodiments, scattered x-rays 118 are collected by x-ray detector 119while specimen positioning system 140 locates and orients specimen 101to produce angularly resolved scattered x-rays.

In some embodiments, a SXR scatterometry system includes one or morephoton counting detectors with high dynamic range (e.g., greater than10⁵). In some embodiments, a single photon counting detector detects theposition and number of detected photons.

In some embodiments, the x-ray detector resolves one or more x-rayphoton energies and produces signals for each x-ray energy componentindicative of properties of the specimen. In some embodiments, the x-raydetector 119 includes any of a CCD array, a microchannel plate, aphotodiode array, a microstrip proportional counter, a gas filledproportional counter, a scintillator, or a fluorescent material.

In this manner the X-ray photon interactions within the detector arediscriminated by energy in addition to pixel location and number ofcounts. In some embodiments, the X-ray photon interactions arediscriminated by comparing the energy of the X-ray photon interactionwith a predetermined upper threshold value and a predetermined lowerthreshold value. In one embodiment, this information is communicated tocomputing system 130 via output signals 135 for further processing andstorage.

Diffraction patterns resulting from simultaneous illumination of aperiodic target with multiple illumination wavelengths are separated atthe detector plane due to angular dispersion in diffraction. In theseembodiments, integrating detectors are employed. The diffractionpatterns are measured using area detectors, e.g., vacuum-compatiblebackside CCD or hybrid pixel array detectors. Angular sampling isoptimized for Bragg peak integration. If pixel level model fitting isemployed, angular sampling is optimized for signal information content.Sampling rates are selected to prevent saturation of zero order signals.

It some examples, it is desirable to perform measurements at largeranges of wavelength, angle of incidence and azimuth angle to increasethe precision and accuracy of measured parameter values. This approachreduces correlations among parameters by extending the number anddiversity of data sets available for analysis.

Measurements of the intensity of diffracted radiation as a function ofillumination wavelength and x-ray incidence angle relative to the wafersurface normal are collected. Information contained in the multiplediffraction orders is typically unique between each model parameterunder consideration. Thus, x-ray scattering yields estimation resultsfor values of parameters of interest with small errors and reducedparameter correlation.

In some embodiments, metrology tool 100 includes a wafer chuck 103 thatfixedly supports wafer 101 and is coupled to specimen positioning system140. Specimen positioning system 140 configured to actively positionspecimen 101 in six degrees of freedom with respect to illumination beam114. In one example, computing system 130 communicates command signals(not shown) to specimen positioning system 140 that indicate the desiredposition of specimen 101. In response, specimen positioning system 140generates command signals to the various actuators of specimenpositioning system 140 to achieve the desired positioning of specimen101.

In a further aspect, a SXR scatterometry system is employed to determineproperties of a specimen (e.g., structural parameter values) based onone or more diffraction orders of scattered light. As depicted in FIG.3, metrology tool 100 includes a computing system 130 employed toacquire signals 135 generated by detector 119 and determine propertiesof the specimen based at least in part on the acquired signals.

SXR scatterometry enables overlay measurements on design-rule targetsbecause the illumination wavelength(s) are shorter than the period ofthe measured structures. This provides a significant benefit overexisting technology where overlay is measured on larger than the designrule targets. Use of SXR wavelengths permits target design at processdesign rules, i.e., no “non-zero offsets”. The reduction in non-zerooffset is the result of reduced pattern placement error and a processoptimized for patterning at the device pitch. Pattern placement errorsdepend on the manufacturing process, which is driven by the patternpitch. As the pattern pitch is reduced, the pattern placement errors arealso reduced. Furthermore, as the manufacturing process is optimized tothe device pitch, local asymmetric deformation of device characteristicpatterns (e.g., device-like patterns) is reduced.

An overlay metrology target for SXR measurements may include a onedimensional periodic array or two-dimensional periodic arrays. Onedimensional targets exhibit large angular divergence along the plane ofincidence, increasing flux and throughput. For two dimensional targetsangular dispersion of diffraction is not equivalent for the two in-planeaxes. Thus, for sample directions parallel to the plane of incidence, anadditional, super-period may be imposed. In these examples, it may beadvantageous to rotate the wafer and perform sequential, orthogonalmeasurements by a single subsystem on the same target.

In one aspect, SXR scatterometry signals are employed to resolve overlayerror based on nonzero diffraction orders. Due to the relatively shortwavelengths of SXR radiation, nonzero diffraction orders, and the +/−1diffraction orders in particular, provide relatively high sensitivity tooverlay error.

In general, target design and associated measurement algorithms differdepending on whether 0th or 1st order scatterometry is employed. For 0thorder scatterometry, each measured area, i.e., cell, of a metrologytarget yields a single 0th order signal as a function of wavelength,incidence angle, and azimuth angle. However, for 1^(st) orderscatterometry, each measured area, i.e., cell, of a metrology targetyields two signals, i.e., the +1 diffraction order signal and the −1diffraction order signal, as a function of wavelength, incidence angle,and azimuth angle. Hence, in some examples, fewer cells are required toextract sufficient signal information to resolve a parameter ofinterest, e.g., overlay or a shape parameter, based on 1st orderscatterometry. In these examples, smaller metrology targets may beemployed. To be effective, 1st order scatterometry requires relativelyhigh pupil uniformity. Calibration methodology to reduce the effect ofpupil non-uniformity is provided in U.S. Patent Publication No.2004/0169861 by Mieher et al, and assigned to KLA-Tencor Corporation,the content of which is incorporated herein by reference in itsentirety.

In a further aspect, SXR scatterometry signals at zero order, nonzeroorders, or any combination thereof, are employed to resolve the valuesof shape parameters characterizing structures under measurements, e.g.,CD, H, SWA, profile parameters, etc. In some embodiments, SXRscatterometry measurements of overlay and shape parameters are performedsimultaneously from SXR scatterometry measurements collected from thesame metrology target. This enables measurements of Edge PlacementErrors (EPE), such as end line shortening, line to contact distance,etc. As a result, SXR scatterometry enables edge placement error (EPE)measurements without errors due to target bias, which occur when overlayand CD measurements are performed on different targets. In addition,simultaneous measurement of overlay and CD structure parameters from thesame metrology target improves both measurement accuracy and throughput.

In one example, an edge placement error between layers is estimatedbased on a measurement of overlay as described herein, and a measurementof a shape parameter based on the intensity measurements within eachx-ray diffraction order measured at multiple, different angles ofincidence and multiple, different azimuth angles. Edge placement errors(EPE) combine overlay and shape parameter (e.g., CD) errors. In oneexample, EPE is a difference between a CD value (e.g., width, W,depicted in FIG. 8) and an overlay value (e.g., overlay, D, depicted inFIG. 8). Thus, a measurement of EPE is streamlined by employing thecomputationally efficient overlay measurement described herein, andusing the same intensity measurement data to estimate the CD parametervalue.

FIG. 4 depicts a top view of device structure 400 that includes activefields 401-404, gates 405-408, and contacts 409-420. FIG. 4 illustratesthe edge placement distance, EPD₁, between gate 407 and contact 418.FIG. 4 also illustrates the edge placement distance, EPD₂, between gate408 and contact 418 and the edge placement distance EPD₃, between gate406 and contact 414. The edge placement distances must be carefullycontrolled to ensure high device yield. If the edge placement errorassociated with any of these edge placement distances is too large, thedevice will fail. As illustrated in FIG. 4, both overlay errors and CDerrors contribute to EPE. For example, EPE results if the layersassociated with the contact are misaligned with the layers associatedwith the gates. Similarly, EPE results if the CD associated with thecontact structures deviates from nominal dimensions. For example,contacts 413 and 416 are too large. The result is overlap between eachcontact and corresponding gate structure and device failure.Furthermore, the three-dimensional shape of each measured structureplays a role. In some examples, the sidewall angle cannot be ignored. Insome of these examples, a structure is characterized by a top CDdimension and a bottom CD dimension, rather than a single CD dimension.

Additional details regarding EPE measurements are described in U.S.Patent Publication No. 2016/0003609 by Shchegrov et al., which isincorporated herein by reference in its entirety.

In some embodiments, SXR scatterometry measurement signals from twocells of a metrology target each having a nominal offset in oppositedirections are employed to resolve overlay error.

FIG. 5A depicts cell 160A having a grating structure 161A stacked aboveanother grating structure 162A. Grating structures 161A and 162A havethe same pitch, but grating structure 161A is offset from gratingstructure 162A by a distance equal to the sum of a nominal offset in onedirection, +OFFSET, and an overlay error, OVL.

FIG. 5B depicts cell 160B having a grating structure 161B stacked aboveanother grating structure 162B. Grating structures 161B and 162B havethe same pitch, but grating structure 161A is offset from gratingstructure 162A by a distance equal to a sum of the nominal offset in theopposite direction compared to metrology target 160A, −OFFSET, and theoverlay error, OVL. Nominally, metrology targets 160A and 160B are thesame, except that the direction of offset of the top grating structurewith respect to the bottom grating structure is opposite.

The value of overlay error, OVL, associated with the measurement targetis based the difference between the detected intensities within a +1diffraction order and a −1 diffraction order associated with cell 160Aand a difference between the detected intensities within the +1diffraction order and the −1 diffraction order associated with cell160B. In one example, computing system 130 determines differencesignals, D₁ and D₂ as illustrated by equation (1), where I_(A) ⁺¹ is themeasured intensity of the +1 order from cell 160A, I_(A) ⁻¹ is themeasured intensity of the −1 order from cell 160A, I_(B) ⁺¹ is themeasured intensity of the +1 order from cell 160B, and I_(B) ⁻¹ is themeasured intensity of the −1 order from cell 160B.

D ₁ =I _(A) ⁺¹ −I _(A) ⁻¹

D ₂ =I _(B) ⁺¹ −I _(B) ⁻¹  (1)

Assuming a linear relationship between the differential signal valuesand the actual offset, overlay error is determined directly from thedifferential signals as illustrated by equation (2), wherein OFFSET isthe magnitude of the nominal offset distance as described hereinbefore.

$\begin{matrix}{{OverlayError} = {{OFFSET}\left( \frac{D_{1} + D_{2}}{D_{1} - D_{2}} \right)}} & (2)\end{matrix}$

In some examples, metrology based on SXR scatterometry involvesdetermining parameters of interest, e.g., overlay error, shapeparameters, etc., characterizing the sample by the inverse solution of apre-determined measurement model with the measured SXR scatterometrydata. The method of inverse solve includes, but is not limited to, modelbased regression, tomography, machine learning, or any combinationthereof. In this manner, target parameters are estimated by solving forvalues of a parameterized measurement model that minimize errors betweenthe measured scattered x-ray intensities and modeled results.

In some embodiments, computing system 130 is configured to generate astructural model (e.g., geometric model, material model, or combinedgeometric and material model) of a measured structure of a specimen,generate a SXR response model that includes at least one geometricparameter from the structural model, and resolve at least one specimenparameter value by performing a fitting analysis of SXR measurement datawith the SXR response model. The analysis engine is used to compare thesimulated SXR signals with measured data thereby allowing thedetermination of geometric as well as material properties such aselectron density of the sample. In the embodiment depicted in FIG. 3,computing system 130 is configured as a model building and analysisengine configured to implement model building and analysis functionalityas described herein.

FIG. 6 is a diagram illustrative of an exemplary model building andanalysis engine 180 implemented by computing system 130. As depicted inFIG. 6, model building and analysis engine 180 includes a structuralmodel building module 181 that generates a structural model 182 of ameasured structure of a specimen. The structural model 182 is receivedas input to SXR response function building module 183. SXR responsefunction building module 183 generates a SXR response function model 184based at least in part on the structural model 182. In some examples,the SXR response function model 184 is based on x-ray form factors, alsoknown as structure factors,

F({right arrow over (q)})=∫ρ({right arrow over (r)})e^(−i{right arrow over (q)}·{right arrow over (r)}) d{right arrow over(r)}  (3)

where F is the form factor, q is the scattering vector, and ρ(r) is theelectron density of the specimen in spherical coordinates. The x-rayscattering intensity is then given by

I({right arrow over (q)})=F*F.  (4)

SXR response function model 184 is received as input to fitting analysismodule 185. The fitting analysis module 185 compares the modeled SXRresponse with the corresponding measured data 135 to determine geometricas well as material properties of the specimen.

In some examples, the fitting of modeled data to experimental data isachieved by minimizing a chi-squared value. For example, for SXRmeasurements, a chi-squared value can be defined as

$\begin{matrix}{\chi_{SXR}^{2} = {\frac{1}{N_{SXR}}\Sigma_{j}^{N_{SXR}}\frac{\left( {{S_{j}^{{SXR}\mspace{14mu} {model}}\left( {v_{1},\ldots \mspace{14mu},v_{L}} \right)} - S_{j}^{{SXR}\mspace{14mu} {experiment}}} \right)^{2}}{\sigma_{{SXR},j}^{2}}}} & (5)\end{matrix}$

Where, S_(j) ^(SXR experiment) is the measured SXR signals 135 in the“channel” j, where the index j describes a set of system parameters suchas diffraction order, energy, angular coordinate, etc. S_(j)^(SXR model)(v₁, . . . ,v_(L)) is the modeled SXR signal S_(i) for the“channel” j, evaluated for a set of structure (target) parameters v₁, .. . ,v_(L), where these parameters describe geometric (CD, sidewallangle, overlay, etc.) and material (electron density, etc.). σ_(SXR,j)is the uncertainty associated with the jth channel. N_(SXR) is the totalnumber of channels in the x-ray metrology. L is the number of parameterscharacterizing the metrology target.

Equation (5) assumes that the uncertainties associated with differentchannels are uncorrelated. In examples where the uncertaintiesassociated with the different channels are correlated, a covariancebetween the uncertainties, can be calculated. In these examples achi-squared value for SXR measurements can be expressed as

$\begin{matrix}{\chi_{SXR}^{2} = {\frac{1}{N_{SXR}}\left( {{{\overset{\rightarrow}{S}}_{j}^{{SXR}.\mspace{14mu} {model}}\left( {v_{1},\ldots \mspace{14mu},v_{M}} \right)} - {\overset{\rightarrow}{S}}_{j}^{{SXR}.\mspace{14mu} {experiment}}} \right)^{T}{V_{SXR}^{- 1}\left( {{{\overset{\rightarrow}{S}}_{j}^{{SXR}.\mspace{14mu} {model}}\left( {v_{1},\ldots \mspace{14mu},v_{M}} \right)} - {\overset{\rightarrow}{S}}_{j}^{{SXR}.\mspace{14mu} {experiment}}} \right)}}} & (6)\end{matrix}$

where, V_(SXR) is the covariance matrix of the SXR channeluncertainties, and T denotes the transpose.

In some examples, fitting analysis module 185 resolves at least onespecimen parameter value by performing a fitting analysis on SXRmeasurement data 135 with the SXR response model 184. In some examples,χ_(SXR) ² is optimized.

As described hereinbefore, the fitting of SXR data is achieved byminimization of chi-squared values. However, in general, the fitting ofSXR data may be achieved by other functions.

The fitting of SXR metrology data is advantageous for any type of SXRtechnology that provides sensitivity to geometric and/or materialparameters of interest. Specimen parameters can be deterministic (e.g.,CD, SWA, etc.) or statistical (e.g., rms height of sidewall roughness,roughness correlation length, etc.) as long as proper models describingSXR beam interaction with the specimen are used.

In general, computing system 130 is configured to access modelparameters in real-time, employing Real Time Critical Dimensioning(RTCD), or it may access libraries of pre-computed models fordetermining a value of at least one specimen parameter value associatedwith the specimen 101. In general, some form of CD-engine may be used toevaluate the difference between assigned CD parameters of a specimen andCD parameters associated with the measured specimen. Exemplary methodsand systems for computing specimen parameter values are described inU.S. Pat. No. 7,826,071, issued on Nov. 2, 2010, to KLA-Tencor Corp.,the entirety of which is incorporated herein by reference.

In some examples, model building and analysis engine 180 improves theaccuracy of measured parameters by any combination of feed sidewaysanalysis, feed forward analysis, and parallel analysis. Feed sidewaysanalysis refers to taking multiple data sets on different areas of thesame specimen and passing common parameters determined from the firstdataset onto the second dataset for analysis. Feed forward analysisrefers to taking data sets on different specimens and passing commonparameters forward to subsequent analyses using a stepwise copy exactparameter feed forward approach. Parallel analysis refers to theparallel or concurrent application of a non-linear fitting methodologyto multiple datasets where at least one common parameter is coupledduring the fitting.

Multiple tool and structure analysis refers to a feed forward, feedsideways, or parallel analysis based on regression, a look-up table(i.e., “library” matching), or another fitting procedure of multipledatasets. Exemplary methods and systems for multiple tool and structureanalysis is described in U.S. Pat. No. 7,478,019, issued on Jan. 13,2009, to KLA-Tencor Corp., the entirety of which is incorporated hereinby reference.

In another further aspect, an initial estimate of values of one or moreparameters of interest is determined based on SXR measurements performedat a single orientation of the incident x-ray beam with respect to themeasurement target. The initial, estimated values are implemented as thestarting values of the parameters of interest for a regression of themeasurement model with measurement data collected from SXR measurementsat multiple orientations. In this manner, a close estimate of aparameter of interest is determined with a relatively small amount ofcomputational effort, and by implementing this close estimate as thestarting point for a regression over a much larger data set, a refinedestimate of the parameter of interest is obtained with less overallcomputational effort.

In some embodiments, the value of overlay error associated with ameasurement target or an in-die target is directly determined fromdetected intensities within one or more nonzero diffraction orders basedon a trained machine-learning based measurement model. In theseembodiments, a trained machine learning based model directly extractsoverlay error from SXR measurement data.

In some embodiments, the target measured by the SXR metrology system isnot periodic, yet the trained machine-learning based measurement modelis able to extract overlay error from the SXR measurements.

In some embodiments, the machine-learning based measurement model istrained based on SXR measurement data collected from Design OfExperiments (DOE) targets having known values of overlay error. In someembodiments, the overlay error associated with the targets is measuredby a trusted reference metrology system, such as an e-beam basedmetrology system, a scanning electron microscope, etc. In someembodiments, the DOE targets are design rule metrology targets. In someembodiments, the DOE targets are in-die active device structures.

In some examples, the machine-learning based measurement model is aneural network model, a support vector machine model, etc. Additionaldetails are described in U.S. Pat. No. 10,352,876 to Shchegrov et al.,and assigned to KLA-Tencor Corporation, the content of which isincorporated herein by reference in its entirety.

In some other embodiments, the machine-learning based measurement modelis trained based on SXR measurement data collected from Design OfExperiments (DOE) targets, e.g., periodic targets, and known values ofoverlay error associated with in-die active device structures in closeproximity to the measured DOE targets. In these embodiments, the overlayerror associated with the in-die active device structures is measured bya trusted reference metrology system, such as an e-beam based metrologysystem, a scanning electron microscope, etc. In this manner, the trainedmachine-learning based measurement model estimates the overlay error ofan in-die active device structure based on SXR scatterometry measurementdata collected from a nearby design rule metrology target.

FIG. 10 illustrates a semiconductor die 190 including active device area191 and scribe line area 192 circumscribing the active device area. Anumber of design rule metrology targets are fabricated within the scribeline area 192, such as design rule target 193. Similarly, a number ofactive device structures are fabricated within active device area 191,such as active device structure 194. In some embodiments, a trainedmachine-learning based measurement model estimates overlay errorassociated with active device structure 194 based on SXR scatterometrymeasurements of design rule target 193.

Additional details regarding machine-learning based measurement modelsare described in U.S. Patent Publication No. 2016/0003609 by Shchegrovet al., which is incorporated herein by reference in its entirety.

In some other embodiments, the machine-learning based measurement modelis trained based on SXR measurement data collected from Design OfExperiments (DOE) targets and the values of shape parameters determinedbased on the SXR measurement data. In some examples, SXR measurementdata is employed to estimate values of shape parameters (e.g., CD, H,SWA, profile parameters, etc.) as described hereinbefore. These shapeparameter values, along with the SXR measurement data, and known valuesof overlay error associated with in-die active device structures inclose proximity to the measured DOE targets are employed to train themachine-learning based measurement models. The additional structuralinformation improves the accuracy of the trained model.

In some embodiments, a SXR scatterometry based overlay measurementinvolves illuminating a sample with SXR radiation and detecting theintensities of the resulting diffraction orders for multiple angles ofincidence relative to the sample, multiple wavelengths, or both.Furthermore, the overlay error associated with the measurement target isdetermined based on modulations in the plurality of intensities withineach of the one or more nonzero diffraction orders at each of themultiple measurement instances.

In these embodiments, SXR scatterometry measurements of a metrologytarget are performed at a number of different angles of incidence andazimuth angles. FIG. 7 depicts wafer 101 including a metrology target120. An x-ray illumination source illuminates metrology target 120 witha beam of x-ray radiation 114 at an angle of incidence, AOI, and anazimuth angle, Az. The angle of incidence and the azimuth angle of thebeam of incident x-ray radiation are defined with respect to acoordinate frame {^(B)X, ^(B)Y, ^(B)Z} fixed to specimen 101. Asdepicted in FIG. 7, metrology target 120 includes a grating structurethat extends in the ^(B)Y direction and is periodic in the ^(B)Xdirection. The angle of incidence is defined as the angle of theprojection of the incident beam onto the ^(B)X-^(B)Z plane with respectto the ^(B)Z axis. In this sense, changes in the angle of incidence canbe viewed as a rotation of wafer 101 about the ^(B)Y axis, in-plane withwafer 101. Similarly, the azimuth angle is defined as the angle of theprojection of the incident beam onto the ^(B)X-^(B)Y plane with respectto the ^(B)X axis. In this sense, changes in the azimuth angle can beviewed as a rotation of wafer 101 about the ^(B)Z axis, normal to wafer101.

As illustrated in FIG. 3, metrology tool 100 includes a specimenpositioning system 140 configured to both align specimen 101 and orientspecimen 101 over a large range of out of plane angular orientationswith respect the SXR scatterometer. In other words, specimen positioningsystem 140 is configured to rotate specimen 101 over a large angularrange about one or more axes of rotation aligned in-plane and normal tothe surface of specimen 101. In this manner, every location on thesurface of specimen 101 is available for measurement over a range ofrotations about the axes of coordinate frame {^(B)X, ^(B)Y, ^(B)Z} fixedto specimen 101.

In the embodiment depicted in FIG. 3, a single beam of incident x-rayradiation is illustrated. The orientation of the single beam withrespect to the wafer is defined by a single nominal angle of incidenceand azimuth angle. For embodiments employing a single illumination beam,x-ray diffraction measurements associated multiple, different angles ofincidence and azimuth angles are performed sequentially. However, ingeneral, x-ray diffraction measurements associated with multiple,different angles of incidence and azimuth angles may be performedsimultaneously. In some embodiments, one or more x-ray sources and oneor more sets of x-ray optics may be employed such that the metrologytarget is illuminated simultaneously from multiple directions, eitherdiscrete or continuous in angle of incidence and azimuth angle.

The vertical stacking of two or more structures in different layers ofthe metrology target affects the x-ray diffracted signal in a strong andunique way when measurements are made at multiple, different angles ofincidence and multiple, different azimuth angles. Thus, the values ofoverlay and shape parameters may be estimated based on the measuredintensities.

In these embodiments, the estimation of overlay involves aparameterization of the intensity modulations of common orders such thata low frequency shape modulation is described by a set, or ratio, ofbasis functions and a high frequency overlay modulation is described byan affine-circular function that includes a parameter indicative of theoverlay.

In one example, the parameterization is derived from an analysis of acanonical overlay problem. FIG. 8 depicts a layered metrology target 150including two periodic arrays of lines 151 and 152, each havingperiodicity, P. The arrays of lines are separated vertically by adistance, S, and the arrays of lines are offset by overlay distance, D.The height and width of each of the arrays of lines are given by theparameters H and W, respectively.

In addition, the electron density of the top line is given by theparameter, δ0, and the bottom line by δ1. The metrology target 150 isilluminated by a beam of x-ray radiation having a wavelength, λ. Thebeam of incident x-ray radiation impinges on the metrology target at anangle of incidence, θ, and an azimuth angle, φ, where φ=0 when theprojection of the light ray is perpendicular to the periodicity of thegrating. For such a grating we define the wavelength number as k₀=2π/λ,and the grating number in the 1D periodic direction as k_(x)=2π/P, wherem is the order number. The angle of incidence is measured from the axisnormal to the wafer.

An analysis of the diffraction intensity provides an approximation ofthe intensity of each order as illustrated in equation (7).

$\begin{matrix}{I = {\left( \frac{k_{0}{WH}}{P\cos \theta} \right)^{2}\sin \; {c\left( \frac{mk_{x}W}{2} \right)}^{2}\sin \; {c\left( \frac{mk_{x}H\; \tan \; \theta \; \cos \; \phi}{2} \right)}^{2}\left( {\delta_{0}^{2} + \delta_{1}^{2} + {2\delta_{0}\delta_{1}{\cos \left( {m{k_{x}\left( {D + {S\; \tan \; {\theta cos\phi}}} \right)}} \right)}}} \right)}} & (7)\end{matrix}$

Equation (7) illustrates that not only is there a modulation of theorder intensity by changing the angle of incidence, θ, but also bychanging the azimuth angle, φ. Stated another way, we can expect amodulation of the diffraction orders from the projection of the gratingperiodic dimension aligned with ^(B)X into the direction aligned with^(B)Z by changing the angle of incidence. In addition, we can expect amodulation of the diffraction orders from the projection of the gratingperiodic dimension aligned with ^(B)X into the direction aligned with^(B)Y by changing the azimuth angle. In addition, changes in angle ofincidence and azimuth angle can be coordinated to accentuate the overlaysignal. For example, changing the azimuth angle can slow the shape andoverlay modulation due to changes in angle of incidence by the scalingfactor, cos(φ). Finally, equation (7) also illustrates that themodulation of the order intensity due to shape parameters W and H aretypically of low spatial frequency relative to the overlay modulationdescribed by the last term of equation (7).

Due to the relatively low spatial frequency modulation due to shape,this modulation can be modeled by a low order polynomial, e.g., a linearor quadratic function. The modulation due to separation distance, S, andoverlay, D, can then be represented by the cosine term illustrated inequation (7). Hence, a simplified model of the intensity for each ordertakes an additive or multiplicative form as illustrated by equations(8a) and (8b), respectively.

$\begin{matrix}{I = {{\sum\limits_{j = 0}^{N}{a_{j}\theta^{j}}} + {b{\cos \left( {m{k_{x}\left( {D + {S\; \tan \; {\theta cos\phi}}} \right)}} \right)}}}} & \left( {8a} \right) \\{I = {\left( {\sum\limits_{j = 0}^{N}{a_{j}\theta^{j}}} \right)\left( {b{\cos \left( {m{k_{x}\left( {D + {S\; \tan \; \theta \; \cos \; \phi}} \right)}} \right)}} \right)}} & \left( {8b} \right)\end{matrix}$

The shape function defined by the first term of equation (8a) and thefirst factor of equation (8b) model the shape modulation as a linearcombination of basis functions, θj, weighted by parameters aj, withoutexplicit knowledge of the shape. As illustrated in equations (8a) and(8b), a monomial basis is employed to describe the shape change.However, in general, any polynomial, rational, or basis set of any kindmay be employed.

The parameter, b, defines the modulation depth. Parameters D and Sdefine the overlay. By changing the angle of incidence, azimuth angle,or both, the resulting data for any order may be fit to the parametersa_(j), b, D, and S using any suitable curve fitting routine. The overlayis given by the fit for the parameter, D.

The simplified model for overlay measurement described hereinbefore isillustrative of a phenomenological approach to modeling the intensityvariations of diffraction orders based on changes in angle of incidenceand azimuth angle. In general, the model can be based on other waveformsand non-polynomial basis functions.

By fitting measured intensity signals to phenomenological, simplefunctions, overlay offsets associated with multiple layers may beestimated in a computationally efficient manner. As a result, themeasurements are performed at a relatively low computational cost andwithout external reference metrology, thus overcoming the limitations ofcurrent methods based on SEM, optical metrology, or other proposed x-raymetrology techniques.

FIG. 9 depicts a plot 170 indicative of simulation of the fittingresults for the metrology target 150 depicted in FIG. 8. Plotline 171depicts a simulation of the normalized intensity of the −2 diffractionorder for a range of angles. Plotline 172 depicts a simulation of thenormalized intensity of the +2 diffraction order for the same range ofangles. Plotline 173 depicts the results of a fitting of the simulateddiffraction intensities by a model of type described with reference toequation (8). As illustrated in FIG. 9, the simplified model describedwith reference to equation (8) provides a close fit to the simulatedintensity values.

As illustrated by equation (8), the overlay modulation is an evenfunction in the diffraction order. Thus, data from both positive andnegative orders may be averaged, or fit jointly. In addition, multipleorders may be fit jointly. In some examples, different ranges in anglespace may be employed for each different diffraction order. Additionaldetails are described in WIPO Publication No. WO2016176502A1 by Hench etal., and assigned to KLA-Tencor Corporation, the content of which isincorporated herein by reference in its entirety.

In some embodiments, the actual device target is aperiodic. Bycalibrating overlay measurements to a reference measurement, SXRscatterometry techniques can be applied to estimate overlay of aperiodicstructures based on measurements of design rule targets with sufficientperiodicity. This effectively overcomes the limitation of scatterometricmeasurements requiring the measured target to be periodic orapproximately periodic.

In some embodiments, calibrated SXR measurements are employed toestimate the overlay error associated with in-die active devicestructures as part of After-Develop Inspection (ADI) process monitoring.In some embodiments, the in-die active device structures are aperiodiclogic devices.

In some embodiments, a single overlay error calibration value isemployed to calibrate SXR scatterometry based overlay measurements. Areference metrology system, e.g., a SEM, is employed to measure overlayerror associated with an in-die active device structure. In addition, aSXR scatterometry system, e.g., SXR scatterometry tool 100, is employedto measure a nearby design rule metrology target. The difference betweenthe overlay error measured by the SXR scatterometry system and theoverlay error measured by the reference metrology system is the overlayerror calibration value. Subsequent overlay measurements of design rulemetrology targets are adjusted by the overlay error calibration value toestimate the overlay error associated with a nearby in-die active devicestructure. More specifically, the overlay error calibration value isadded to the overlay error measured by the SXR scatterometer to estimatethe overlay error associated with the in-die active device structure. Alimitation of this approach is that it does not compensate for targeterrors induced by the fact that the reference measurement is performedon a different target than the SXR measurements.

In one example, this limitation is overcome by feeding a SEM calibrationbackward to an SXR measurement of the in-die active device structure.This approach is feasible in cases where the SXR measurement does notimpose any physical changes to the measured target.

In another example, two overlay error calibration values are employed tocalibrate SXR scatterometry based overlay measurements. In theseembodiments, a reference metrology system, e.g., a SEM, is employed tomeasure overlay error associated with an in-die active device structureand to measure overlay error associated with a nearby design rulemetrology target. In addition, a SXR scatterometry system, e.g., SXRscatterometry tool 100, is employed to measure overlay error associatedwith the nearby design rule metrology target. The difference between theoverlay error associated with the in-die active device structuremeasured by the reference metrology system and the overlay errorassociated with the nearby design rule metrology target is the firstoverlay error calibration value. The difference between the overlayerror associated with the design rule target measured by the referencemetrology system and the overlay error associated with the design ruletarget measured by SXR scatterometry system is the second overlay errorcalibration value. Subsequent overlay measurements of design rulemetrology targets are adjusted by both the first and second overlayerror calibration values to estimate the overlay error associated with anearby in-die active device structure. More specifically, the first andsecond overlay error calibration values are added to the overlay errormeasured by the SXR scatterometer to estimate the overlay errorassociated with the in-die active device structure.

Reference measurements performed by a e-beam tool may be performed athigh voltage, e.g., 10 kV or higher, to image one or more underlyinglayers. In general, the landing energy of the e-beam tool may beadjusted to maximize imaging performance.

A SXR scatterometry tool as described herein is capable of performingmany different types of measurements related to semiconductormanufacturing. For example, a SXR scatterometry tool may be employed tomeasure characteristics of one or more targets, such as criticaldimensions, overlay, sidewall angles, film thicknesses, process-relatedparameters (e.g., focus and/or dose), etc. The measurement targets mayinclude regions of interest that are periodic, such as gratings in amemory die. Measurement targets may include multiple layers, and thethickness of one or more layers may be measured by a SXR scatterometrytool. Measurement targets may be located within a scribe line or locatedwithin the die itself. In some embodiments, multiple targets aremeasured simultaneously or sequentially by one or more metrology toolsas described in U.S. Pat. No. 7,478,019, the content of which isincorporated herein by reference it its entirety. The data from suchmeasurements may be combined and used in a semiconductor manufacturingprocess, for example, to feed-forward, feed-backward, and feed-sidewayscorrections to the process (e.g., lithography, etch).

Measurement of parameters of interest usually involves a number ofalgorithms. In some embodiments, the interaction of the incident beamwith the sample is modeled using an electro-magnetic solver and usessuch algorithms as RCWA, FEM, method of moments, surface integralmethod, volume integral method, FDTD, Born approximation (BA),Distorted-Wave BA (DWBA), and others. For a model-based measurement, thetarget of interest is usually modeled using a geometric engine, or insome cases, a process modeling engine, or a combination of both. Ageometric engine is implemented, for example, in AcuShape softwareavailable from KLA Corporation (Milpitas, Calif.).

Collected data may be analyzed by a number of data fitting andoptimization techniques and technologies including libraries,Fast-reduced-order models; regression; machine-learning algorithms suchas neural networks, support-vector machines (SVM);dimensionality-reduction algorithms such as, e.g., PCA (principalcomponent analysis), ICA (independent component analysis), LLE(local-linear embedding); sparse representation such as Fourier orwavelet transform; Kalman filter; algorithms to promote matching fromsame or different tool types, and others. Collected data may also beanalyzed by algorithms that do not include modeling, optimization and/orfitting to extract dimensional and material information about thestructure.

In some embodiments, a multiple layer overlay metrology target isdesigned such that the set of separation parameters between eachcombination of two layers is distinct and the minimum separationdistance between all layer combinations is maximized subject to aconstraint on the overall height of the metrology target.

In some embodiments, a multiple layer overlay metrology target isdesigned with different pitch at different layers such that adiffraction order arising from one layer constructively interferes witha different diffraction order of another layer. In one embodiment, aperiodic grating structure located in a first layer has a pitch equal to2 A, where A is an arbitrary, positive valued constant. Another periodicstructure located in a different layer has a pitch equal to 3 A. In thisexample, the second diffraction order of the first layer constructivelyinterferes with the third diffraction order of the second layer. Thus,the intensity measurements detected at these order pairs are dominatedby overlay between the two layers. Conversely, intensity measurementsdetected at different order number pairs not subject to constructiveinterference in overlay are dominated by shape parameters. Thus, in someembodiments, a metrology overlay target is designed with specificgrating structures to increase sensitivity to overlay at specificgrating order pairs, and also provide intensity data useful forestimation of shape parameter values.

Similarly, a multiple layer overlay metrology target is designed withdifferent pitch orientations at different layers such that a diffractionorder arising from one layer constructively interferes with a differentdiffraction order of another layer. In general, a set of layers havingdifferent periodicities (e.g., different grating pitches), differentpitch orientations, or any combination thereof, gives rise to a set ofscattering vectors, each associated with a different layer. The overlaymetrology target is designed such that a predetermined subset of thescattering vectors are aligned. In this manner, the sensitivity tooverlay among the layers corresponding with the predetermined subset ofscattering vectors is enhanced.

In general, an overlay metrology target may include 1D-periodicstructures, i.e., with periodicity in one direction and constant in theother, 2D periodic structures, i.e., periodic in two directions, or anycombination thereof. For 2D-periodic targets, the two directions ofperiodicity may or may not be perpendicular to each other. Moreover, thepitch of each of the constituent structures may be the same ordifferent.

It should be recognized that the various steps described throughout thepresent disclosure may be carried out by a single computer system 130or, alternatively, a multiple computer system 130. Moreover, differentsubsystems of the system 100, such as the specimen positioning system140, may include a computer system suitable for carrying out at least aportion of the steps described herein. Therefore, the aforementioneddescription should not be interpreted as a limitation on the presentinvention but merely an illustration. Further, the one or more computingsystems 130 may be configured to perform any other step(s) of any of themethod embodiments described herein.

In addition, the computer system 130 may be communicatively coupled tothe detector 119 and the illumination optics 115 in any manner known inthe art. For example, the one or more computing systems 130 may becoupled to computing systems associated with detector 119 and theillumination optics 115, respectively. In another example, any ofdetector 119 and illumination optics 115 may be controlled directly by asingle computer system coupled to computer system 130.

The computer system 130 may be configured to receive and/or acquire dataor information from the subsystems of the system (e.g., detector 119 andillumination optics 115, and the like) by a transmission medium that mayinclude wireline and/or wireless portions. In this manner, thetransmission medium may serve as a data link between the computer system130 and other subsystems of the system 100.

Computer system 130 of the metrology system 100 may be configured toreceive and/or acquire data or information (e.g., measurement results,modeling inputs, modeling results, etc.) from other systems by atransmission medium that may include wireline and/or wireless portions.In this manner, the transmission medium may serve as a data link betweenthe computer system 130 and other systems (e.g., memory on-boardmetrology system 100, external memory, or external systems). Forexample, the computing system 130 may be configured to receivemeasurement data (e.g., signals 135) from a storage medium (i.e., memory132 or 190) via a data link. For instance, scatterometry data collectedby detector 119 may be stored in a permanent or semi-permanent memorydevice (e.g., memory 132 or 190). In this regard, the measurementresults may be imported from on-board memory or from an external memorysystem. Moreover, the computer system 130 may send data to other systemsvia a transmission medium. For instance, overlay values 186 determinedby computer system 130 may be stored in a permanent or semi-permanentmemory device (e.g., memory 190). In this regard, measurement resultsmay be exported to another system.

Computing system 130 may include, but is not limited to, a personalcomputer system, mainframe computer system, workstation, image computer,parallel processor, or any other device known in the art. In general,the term “computing system” may be broadly defined to encompass anydevice having one or more processors, which execute instructions from amemory medium.

Program instructions 134 implementing methods such as those describedherein may be transmitted over a transmission medium such as a wire,cable, or wireless transmission link. For example, as illustrated inFIG. 3, program instructions stored in memory 132 are transmitted toprocessor 131 over bus 133. Program instructions 134 are stored in acomputer readable medium (e.g., memory 132). Exemplary computer-readablemedia include read-only memory, a random access memory, a magnetic oroptical disk, or a magnetic tape.

In some embodiments, a scatterometry analysis as described herein isimplemented as part of a fabrication process tool. Examples offabrication process tools include, but are not limited to, lithographicexposure tools, film deposition tools, implant tools, and etch tools. Inthis manner, the results of a SXR scatterometry analysis are used tocontrol a fabrication process. In one example, SXR scatterometymeasurement data collected from one or more targets is sent to afabrication process tool. The SXR scatterometry measurement data isanalyzed as described herein and the results used to adjust theoperation of the fabrication process tool.

Scatterometry measurements as described herein may be used to determinecharacteristics of a variety of semiconductor structures. Exemplarystructures include, but are not limited to, FinFETs, low-dimensionalstructures such as nanowires or graphene, sub 10 nm structures,lithographic structures, through substrate vias (TSVs), memorystructures such as DRAM, DRAM 4F2, FLASH, MRAM and high aspect ratiomemory structures. Exemplary structural characteristics include, but arenot limited to, geometric parameters such as line edge roughness, linewidth roughness, pore size, pore density, side wall angle, profile,critical dimension, pitch, and material parameters such as electrondensity, composition, grain structure, morphology, stress, strain, andelemental identification.

FIG. 11 illustrates a method 200 suitable for implementation bymetrology system 100 of the present invention. In one aspect, it isrecognized that data processing blocks of method 200 may be carried outvia a pre-programmed algorithm executed by one or more processors ofcomputing system 130. While the following description is presented inthe context of metrology system 100, it is recognized herein that theparticular structural aspects of metrology system 100 do not representlimitations and should be interpreted as illustrative only.

In block 201, a first instance of a design rule target disposed on asubstrate is illuminated with a beam of Soft X-Ray (SXR) radiationhaving energy in a range between 10 and 5,000 electronvolts. The designrule target is a multiple layer target;

In block 202, a first plurality of intensities associated with the +1/−1diffraction order of an amount of SXR radiation scattered from the firstinstance of the design rule target is detected in response to theincident beam of SXR radiation.

In block 203, a first value of overlay error associated with the firstinstance of the design rule target is estimated based on the firstplurality of detected intensities within the +1/−1 diffraction order.

In block 204, a value of overlay error associated with a first in-dieactive device structure is estimated based on a measurement of the firstin-die active device structure by a scanning electron microscope. Thefirst instance of the design rule target and the first in-die activedevice structure are fabricated in accordance with the same fabricationprocess rules.

In block 205, an overlay calibration value is determined based on thevalue of overlay error associated with the first in-die active devicestructure and the first value of overlay error associated with the firstinstance of the design rule target.

As described herein, the term “critical dimension” includes any criticaldimension of a structure (e.g., bottom critical dimension, middlecritical dimension, top critical dimension, sidewall angle, gratingheight, etc.), a critical dimension between any two or more structures(e.g., distance between two structures), and a displacement between twoor more structures (e.g., overlay displacement between overlayinggrating structures, etc.). Structures may include three dimensionalstructures, patterned structures, overlay structures, etc.

As described herein, the term “critical dimension application” or“critical dimension measurement application” includes any criticaldimension measurement.

As described herein, the term “metrology system” includes any systememployed at least in part to characterize a specimen in any aspect,including critical dimension applications and overlay metrologyapplications. However, such terms of art do not limit the scope of theterm “metrology system” as described herein. In addition, the metrologysystems described herein may be configured for measurement of patternedwafers and/or unpatterned wafers. The metrology system may be configuredas a LED inspection tool, edge inspection tool, backside inspectiontool, macro-inspection tool, or multi-mode inspection tool (involvingdata from one or more platforms simultaneously), and any other metrologyor inspection tool that benefits from imaging or structures undermeasurement.

Various embodiments are described herein for a semiconductor processingsystem (e.g., a metrology system or a lithography system) that may beused for processing a specimen. The term “specimen” is used herein torefer to a wafer, a reticle, or any other sample that may be processed(e.g., printed or inspected for defects) by means known in the art.

As used herein, the term “wafer” generally refers to substrates formedof a semiconductor or non-semiconductor material. Examples include, butare not limited to, monocrystalline silicon, gallium arsenide, andindium phosphide. Such substrates may be commonly found and/or processedin semiconductor fabrication facilities. In some cases, a wafer mayinclude only the substrate (i.e., bare wafer). Alternatively, a wafermay include one or more layers of different materials formed upon asubstrate. One or more layers formed on a wafer may be “patterned” or“unpatterned.” For example, a wafer may include a plurality of dieshaving repeatable pattern features.

A “reticle” may be a reticle at any stage of a reticle fabricationprocess, or a completed reticle that may or may not be released for usein a semiconductor fabrication facility. A reticle, or a “mask,” isgenerally defined as a substantially transparent substrate havingsubstantially opaque regions formed thereon and configured in a pattern.The substrate may include, for example, a glass material such asamorphous SiO₂. A reticle may be disposed above a resist-covered waferduring an exposure step of a lithography process such that the patternon the reticle may be transferred to the resist.

One or more layers formed on a wafer may be patterned or unpatterned.For example, a wafer may include a plurality of dies, each havingrepeatable pattern features. Formation and processing of such layers ofmaterial may ultimately result in completed devices. Many differenttypes of devices may be formed on a wafer, and the term wafer as usedherein is intended to encompass a wafer on which any type of deviceknown in the art is being fabricated.

In one or more exemplary embodiments, the functions described may beimplemented in hardware, software, firmware, or any combination thereof.If implemented in software, the functions may be stored on ortransmitted over as one or more instructions or code on acomputer-readable medium. Computer-readable media includes both computerstorage media and communication media including any medium thatfacilitates transfer of a computer program from one place to another. Astorage media may be any available media that can be accessed by ageneral purpose or special purpose computer. By way of example, and notlimitation, such computer-readable media can comprise RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium that can be used to carryor store desired program code means in the form of instructions or datastructures and that can be accessed by a general-purpose orspecial-purpose computer, or a general-purpose or special-purposeprocessor. Also, any connection is properly termed a computer-readablemedium. For example, if the software is transmitted from a website,server, or other remote source using a coaxial cable, fiber optic cable,twisted pair, digital subscriber line (DSL), or wireless technologiessuch as infrared, radio, and microwave, then the coaxial cable, fiberoptic cable, twisted pair, DSL, or wireless technologies such asinfrared, radio, and microwave are included in the definition of medium.Disk and disc, as used herein, includes compact disc (CD), laser disc,XRF disc, digital versatile disc (DVD), floppy disk and blu-ray discwhere disks usually reproduce data magnetically, while discs reproducedata optically with lasers. Combinations of the above should also beincluded within the scope of computer-readable media.

Although certain specific embodiments are described above forinstructional purposes, the teachings of this patent document havegeneral applicability and are not limited to the specific embodimentsdescribed above. Accordingly, various modifications, adaptations, andcombinations of various features of the described embodiments can bepracticed without departing from the scope of the invention as set forthin the claims.

What is claimed is:
 1. A metrology system comprising: a Soft X-Ray (SXR)illumination source configured to illuminate a first instance of ameasurement target disposed on a substrate with a beam of SXR radiationhaving energy in a range between 10 and 5,000 electronvolts, wherein themeasurement target includes a first structure disposed in a first layerfabricated at a first height above the substrate and a second structuredisposed in a second layer fabricated at a second height above thesubstrate; an x-ray detector configured to detect a plurality ofintensities each associated with one or more nonzero diffraction ordersof an amount of x-ray radiation scattered from the measurement target inresponse to the incident beam of x-ray radiation; and a computing systemconfigured to estimate a value of overlay error associated with themeasurement target or a corresponding in-die active device structurebased on the plurality of detected intensities within each of the one ormore nonzero diffraction orders.
 2. The metrology system of claim 1, thex-ray detector further configured to detect an intensity associated witha zero diffraction order of the amount of x-ray radiation scattered fromthe measurement target in response to the incident beam of x-rayradiation, and the computing system further configured to: estimate avalue of one or more parameters characterizing a shape of themeasurement target based on the detected intensity within the zerodiffraction order, the plurality of detected intensities within each ofthe one or more nonzero x-ray diffraction orders, or any combinationthereof; and estimate a value of an edge placement error of themeasurement target based on the estimated value of overlay and theestimated value of the one or more parameters characterizing the shapeof the measurement target.
 3. The metrology system of claim 1, the SXRillumination source further configured to illuminate a second instanceof the measurement target disposed on the substrate with the beam of SXRradiation, wherein the first structure of the first instance of themeasurement target is offset from the second structure of the firstinstance of the measurement target by an offset distance in a directionaligned with the first layer, wherein the first structure of the secondinstance of the measurement target is offset from the second structureof the second instance of the measurement target by the offset distancein a direction opposite the direction aligned with the first layer, thex-ray detector configured to detect a plurality of intensities eachassociated with one or more nonzero diffraction orders of an amount ofx-ray radiation scattered from the second instance of the measurementtarget in response to the incident beam of x-ray radiation, and whereinthe estimating of the value of overlay error associated with themeasurement target is based on difference between the detectedintensities within a +1 diffraction order and a −1 diffraction orderassociated with the first instance of the measurement target and adifference between the detected intensities within the +1 diffractionorder and the −1 diffraction order associated with the second instanceof the measurement target.
 4. The metrology system of claim 1, whereinthe estimating of the value of overlay error associated with themeasurement target is based on a fitting analysis of the detectedintensities within the one or more nonzero diffraction orders with aphysically based measurement model.
 5. The metrology system of claim 1,wherein the beam of SXR radiation is incident on the measurement targetat multiple measurement instances, each at a different nominal angle ofincidence, a different nominal azimuth angle, or both.
 6. The metrologysystem of claim 5, wherein the estimating of the value of overlay errorassociated with the measurement target is based on modulations in theplurality of intensities within each of the one or more nonzerodiffraction orders at each of the multiple measurement instances.
 7. Themetrology system of claim 1, wherein the value of overlay errorassociated with the measurement target or the corresponding in-dietarget is directly determined from the detected intensities within theone or more nonzero diffraction orders by a trained machine learningbased measurement model.
 8. The metrology system of claim 7, wherein themeasurement target is not periodic.
 9. The metrology system of claim 7,the Soft X-Ray (SXR) illumination source further configured toilluminate a plurality of Design Of Experiments (DOE) measurementtargets with the beam of SXR radiation having energy in a range between10 and 5,000 electronvolts, the x-ray detector further configured todetect a plurality of intensities each associated with one or morenonzero diffraction orders of an amount of x-ray radiation scatteredfrom each of the plurality of DOE measurement targets in response to theincident beam of x-ray radiation, the computing system furtherconfigured to train the machine learning based measurement model basedon the detected plurality of intensities and known values of overlayerror associated with each of the DOE measurement targets orcorresponding in-die targets.
 10. The metrology system of claim 9,wherein the known values of overlay error are determined frommeasurements of the DOE measurement targets or corresponding in-dietargets by a reference metrology system.
 11. The metrology system ofclaim 1, wherein the determining of the value of overlay errorassociated with the corresponding in-die active device structureinvolves a summation of the estimated value of overlay error associatedwith the measurement target based on the plurality of detectedintensities within each of the one or more nonzero diffraction ordersand a correction value.
 12. The metrology system of claim 1, wherein thefirst layer is a resist layer.
 13. The metrology system of claim 1,wherein the measurement target is a design rule target disposed within ascribe line or an in-die active device structure.
 14. The metrologysystem of claim 1, wherein the corresponding in-die active devicestructure is not periodic.
 15. A method comprising: illuminating a firstinstance of a design rule target disposed on a substrate with a beam ofSoft X-Ray (SXR) radiation having energy in a range between 10 and 5,000electronvolts, wherein the design rule target is a multiple layertarget; detecting a first plurality of intensities associated with the+1/−1 diffraction order of an amount of SXR radiation scattered from thefirst instance of the design rule target in response to the incidentbeam of SXR radiation; estimating a first value of overlay errorassociated with the first instance of the design rule target based onthe first plurality of detected intensities within the +1/−1 diffractionorder; estimating a value of overlay error associated with a firstin-die active device structure based on a measurement of the firstin-die active device structure by a scanning electron microscope,wherein the first instance of the design rule target and the firstin-die active device structure are fabricated in accordance with thesame fabrication process rules; and determining an overlay calibrationvalue based on the value of overlay error associated with the firstin-die active device structure and the first value of overlay errorassociated with the first instance of the design rule target.
 16. Themethod of claim 15, further comprising: illuminating a second instanceof the design rule target with a beam of SXR radiation having energy ina range between 10 and 5,000 electronvolts; detecting a second pluralityof intensities each associated with the +1/−1 diffraction order of anamount of SXR radiation scattered from the second instance of the designrule target in response to the incident beam of SXR radiation;estimating a value of overlay error associated with the second instanceof the design rule target based on the plurality of detected intensitieswithin the +1/−1 diffraction order; estimating a value of overlay errorassociated with a second in-die active device structure based on asummation of the value of overlay error associated with the secondinstance of the design rule target and the overlay calibration value.17. The method of claim 15, wherein the overlay calibration value is thedifference between the value of overlay error associated with the firstin-die active device structure and the first value of overlay errorassociated with the first instance of the design rule target.
 18. Themethod of claim 15, further comprising: estimating a second value ofoverlay error associated with the first instance of the design ruletarget based on a measurement of the first instance of the design ruletarget by the scanning electron microscope, wherein the determining ofthe overlay calibration value is based on a difference between the firstand second values of overlay error associated with the first instance ofthe design rule target and a difference between the value of overlayerror associated with the first in-die active device structure and thesecond value of overlay error associated with the first instance of thedesign rule target.
 19. The method of claim 16, further comprising:detecting an intensity associated with a zero diffraction order of theamount of x-ray radiation scattered from the second instance of thedesign rule target in response to the incident beam of x-ray radiation;estimating a value of one or more parameters characterizing a shape ofthe design rule target based on the detected intensity within the zerodiffraction order, the plurality of detected intensities within the+1/−1 diffraction order, or any combination thereof; and estimating avalue of an edge placement error of the second instance of the designrule target based on the value of overlay error associated with thesecond instance of the design rule target and the value of the one ormore parameters characterizing the shape of the second instance of thedesign rule target.
 20. The method of claim 15, wherein the design ruletarget is disposed within a scribe line or is an in-die active devicestructure.
 21. The method of claim 15, wherein the in-die active devicestructure is not periodic.
 22. A metrology system comprising: a SoftX-Ray (SXR) illumination source configured to illuminate a firstinstance of a measurement target disposed on a substrate with a beam ofSXR radiation having energy in a range between 10 and 5,000electronvolts, wherein the measurement target includes a first structuredisposed in a first layer fabricated at a first height above thesubstrate and a second structure disposed in a second layer fabricatedat a second height above the substrate; an x-ray detector configured todetect a plurality of intensities each associated with one or morenonzero diffraction orders of an amount of x-ray radiation scatteredfrom the measurement target in response to the incident beam of x-rayradiation; and a non-transitory, computer-readable medium storinginstructions that, when executed by one or more processors, causes theone or more processors to: estimate a value of overlay error associatedwith the measurement target or a corresponding in-die active devicestructure based on the plurality of detected intensities within each ofthe one or more nonzero diffraction orders.